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InstantDL: An easy-to-use deep learning pipeline for image segmentation and classification.
BMC Bioinformatics 22:103 (2021)
BACKGROUND: Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. RESULTS: We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. CONCLUSIONS: With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 1471-2105
Journal BMC Bioinformatics
Quellenangaben Volume: 22, Issue: 1, Article Number: 103
Publisher BioMed Central
Publishing Place Campus, 4 Crinan St, London N1 9xw, England
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme